Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "57" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 31 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459848 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 40.375309 | 1.080397 | 10.605565 | 1.854619 | 7.160995 | 0.252455 | 3.108833 | 0.622519 | 0.5743 | 0.7636 | 0.3672 | 3.227020 | 2.760799 |
| 2459847 | RF_maintenance | 100.00% | 3.76% | 0.00% | 0.00% | 100.00% | 0.00% | 43.812050 | 0.678705 | 7.731519 | -0.715950 | 7.331711 | 0.716317 | 9.682553 | 0.118626 | 0.5803 | 0.6991 | 0.4012 | 3.775459 | 2.833082 |
| 2459846 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 37.333429 | 0.826479 | 12.837631 | 0.811353 | 8.768892 | 2.370139 | 11.437697 | 0.780392 | 0.7600 | 0.6981 | 0.3857 | 4.262421 | 2.870334 |
| 2459845 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 48.164072 | 0.941380 | 14.439773 | 1.981042 | 5.123085 | 0.778799 | 3.601151 | 0.231211 | 0.5934 | 0.7658 | 0.3655 | 4.877142 | 9.150308 |
| 2459844 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 3.557460 | 0.911637 | -0.309295 | 1.803977 | 3.311608 | -0.217249 | 6.789452 | -1.177304 | 0.0293 | 0.0263 | 0.0022 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | 55.185849 | 0.520442 | 5.401220 | 1.614225 | 9.199928 | 0.522061 | 2.345635 | 1.137458 | 0.5966 | 0.7702 | 0.3725 | 6.199082 | 4.235744 |
| 2459840 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 2.272253 | 2.619958 | -0.499442 | 0.342779 | -0.950273 | -1.049960 | -0.010017 | -0.258839 | 0.7404 | 0.8307 | 0.8009 | nan | nan |
| 2459839 | RF_maintenance | 0.00% | - | - | - | - | - | -0.096804 | -0.263088 | -1.043329 | 1.311894 | 0.543853 | -0.245044 | -0.160606 | -0.608106 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 58.301410 | 83.628423 | 63.256506 | 69.746276 | 60.058294 | 69.085117 | 577.857305 | 616.726658 | 0.0183 | 0.0167 | 0.0014 | 1.080064 | 1.077981 |
| 2459836 | RF_maintenance | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0421 | 0.0359 | 0.0049 | nan | nan |
| 2459835 | RF_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 1.979614 | -1.362596 | 1.422190 | -0.318234 | -0.466979 | -0.386789 | -0.385043 | -0.014130 | 0.0412 | 0.0351 | 0.0033 | nan | nan |
| 2459833 | RF_maintenance | 0.00% | 100.00% | 100.00% | 0.00% | - | - | 2.326651 | -0.751501 | -0.053379 | -1.153095 | 1.176306 | 3.827846 | 2.232919 | 0.207740 | 0.0397 | 0.0355 | 0.0052 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 52.385012 | -0.621665 | 7.804106 | 1.726837 | 9.472987 | -0.256581 | 6.409423 | 0.898648 | 0.1158 | 0.0843 | 0.0187 | 1.209536 | 1.212818 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.229600 | -0.439949 | 4.709024 | 2.703703 | 2.573753 | 1.900663 | 0.604808 | -0.195424 | 0.0427 | 0.0459 | 0.0078 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 49.170711 | -0.618348 | 11.393398 | 2.644989 | 20.964648 | 1.196897 | 12.307146 | 1.307550 | 0.1200 | 0.0697 | 0.0170 | 1.234562 | 1.239000 |
| 2459829 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 63.536297 | 0.093389 | 10.764298 | 0.319359 | 14.447397 | -0.272346 | 14.994163 | 6.959132 | 0.1094 | 0.0790 | 0.0232 | 12.614907 | 24.542665 |
| 2459828 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 36.581251 | -0.272343 | 10.066689 | 1.841372 | 16.428973 | 2.929756 | 13.737570 | 4.650430 | 0.1146 | 0.0745 | 0.0180 | 1.238395 | 1.253940 |
| 2459827 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 48.334575 | -0.274425 | 10.582060 | 3.432361 | 11.447484 | -0.001403 | 2.443265 | 1.534613 | 0.1119 | 0.0768 | 0.0242 | 1.168364 | 1.169557 |
| 2459826 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459825 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 36.653906 | -0.328046 | 10.520466 | 1.665801 | 12.317937 | 1.229100 | 1.012685 | 0.285645 | 0.1258 | 0.0898 | 0.0243 | 1.255158 | 1.235506 |
| 2459824 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 45.120606 | -0.403499 | 7.246983 | 2.457348 | 3.950301 | -0.026168 | 5.337314 | 5.164208 | 0.1087 | 0.0807 | 0.0316 | 1.160059 | 1.167462 |
| 2459823 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 26.749726 | -0.917430 | 15.967771 | 2.461596 | 13.280491 | 2.190056 | 20.948857 | 7.199814 | 0.1144 | 0.0843 | 0.0251 | 0.856762 | 0.856478 |
| 2459822 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 32.628819 | -1.093990 | 14.026791 | 2.521759 | 11.282870 | 1.495692 | 3.690788 | 1.645296 | 0.1234 | 0.1007 | 0.0235 | 1.176799 | 1.177095 |
| 2459821 | RF_maintenance | 100.00% | 11.29% | 11.29% | 0.00% | 100.00% | 0.00% | 35.086668 | -1.403747 | 16.755333 | 0.567621 | 7.862217 | 0.773151 | -0.680038 | -0.638820 | 0.6146 | 0.5952 | 0.3068 | 5.974096 | 3.094289 |
| 2459820 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 46.247154 | -0.914049 | 10.789767 | 2.985319 | 31.535230 | 1.455193 | 8.460077 | 10.621224 | 0.6482 | 0.7083 | 0.3555 | 4.698082 | 3.467920 |
| 2459817 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 29.645983 | -0.435206 | 14.125798 | 1.979171 | 11.625131 | 2.643756 | 1.143964 | 0.713251 | 0.7125 | 0.6968 | 0.3521 | 4.990711 | 2.373038 |
| 2459816 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
| 2459815 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 27.131631 | -1.638879 | 18.472531 | 0.265281 | 16.974436 | 0.702329 | 18.125497 | 1.340584 | 0.7070 | 0.7042 | 0.3709 | 5.266770 | 3.043185 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 56.623890 | -2.097081 | 11.182618 | 0.569005 | 37.951611 | 0.752154 | 18.940491 | 3.867694 | 0.6655 | 0.7243 | 0.3681 | 13.102847 | 10.527397 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 40.375309 | 1.080397 | 40.375309 | 1.854619 | 10.605565 | 0.252455 | 7.160995 | 0.622519 | 3.108833 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 43.812050 | 0.678705 | 43.812050 | -0.715950 | 7.731519 | 0.716317 | 7.331711 | 0.118626 | 9.682553 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 37.333429 | 37.333429 | 0.826479 | 12.837631 | 0.811353 | 8.768892 | 2.370139 | 11.437697 | 0.780392 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 48.164072 | 0.941380 | 48.164072 | 1.981042 | 14.439773 | 0.778799 | 5.123085 | 0.231211 | 3.601151 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Temporal Discontinuties | 6.789452 | 3.557460 | 0.911637 | -0.309295 | 1.803977 | 3.311608 | -0.217249 | 6.789452 | -1.177304 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 55.185849 | 0.520442 | 55.185849 | 1.614225 | 5.401220 | 0.522061 | 9.199928 | 1.137458 | 2.345635 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Shape | 2.619958 | 2.272253 | 2.619958 | -0.499442 | 0.342779 | -0.950273 | -1.049960 | -0.010017 | -0.258839 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Power | 1.311894 | -0.263088 | -0.096804 | 1.311894 | -1.043329 | -0.245044 | 0.543853 | -0.608106 | -0.160606 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Temporal Discontinuties | 616.726658 | 83.628423 | 58.301410 | 69.746276 | 63.256506 | 69.085117 | 60.058294 | 616.726658 | 577.857305 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 1.979614 | -1.362596 | 1.979614 | -0.318234 | 1.422190 | -0.386789 | -0.466979 | -0.014130 | -0.385043 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Temporal Variability | 3.827846 | -0.751501 | 2.326651 | -1.153095 | -0.053379 | 3.827846 | 1.176306 | 0.207740 | 2.232919 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 52.385012 | 52.385012 | -0.621665 | 7.804106 | 1.726837 | 9.472987 | -0.256581 | 6.409423 | 0.898648 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Power | 4.709024 | -0.229600 | -0.439949 | 4.709024 | 2.703703 | 2.573753 | 1.900663 | 0.604808 | -0.195424 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 49.170711 | 49.170711 | -0.618348 | 11.393398 | 2.644989 | 20.964648 | 1.196897 | 12.307146 | 1.307550 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 63.536297 | 0.093389 | 63.536297 | 0.319359 | 10.764298 | -0.272346 | 14.447397 | 6.959132 | 14.994163 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 36.581251 | -0.272343 | 36.581251 | 1.841372 | 10.066689 | 2.929756 | 16.428973 | 4.650430 | 13.737570 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 48.334575 | 48.334575 | -0.274425 | 10.582060 | 3.432361 | 11.447484 | -0.001403 | 2.443265 | 1.534613 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 36.653906 | -0.328046 | 36.653906 | 1.665801 | 10.520466 | 1.229100 | 12.317937 | 0.285645 | 1.012685 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 45.120606 | 45.120606 | -0.403499 | 7.246983 | 2.457348 | 3.950301 | -0.026168 | 5.337314 | 5.164208 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 26.749726 | -0.917430 | 26.749726 | 2.461596 | 15.967771 | 2.190056 | 13.280491 | 7.199814 | 20.948857 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 32.628819 | 32.628819 | -1.093990 | 14.026791 | 2.521759 | 11.282870 | 1.495692 | 3.690788 | 1.645296 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 35.086668 | -1.403747 | 35.086668 | 0.567621 | 16.755333 | 0.773151 | 7.862217 | -0.638820 | -0.680038 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 46.247154 | 46.247154 | -0.914049 | 10.789767 | 2.985319 | 31.535230 | 1.455193 | 8.460077 | 10.621224 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 29.645983 | 29.645983 | -0.435206 | 14.125798 | 1.979171 | 11.625131 | 2.643756 | 1.143964 | 0.713251 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 27.131631 | -1.638879 | 27.131631 | 0.265281 | 18.472531 | 0.702329 | 16.974436 | 1.340584 | 18.125497 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 57 | N04 | RF_maintenance | ee Shape | 56.623890 | -2.097081 | 56.623890 | 0.569005 | 11.182618 | 0.752154 | 37.951611 | 3.867694 | 18.940491 |